#!pip install pycaret --user

from pycaret.classification import *

from sklearn import datasets
import datetime
from datetime import date
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn
import shap

import xgboost as xgb
from xgboost import XGBClassifier, XGBRegressor
from xgboost import to_graphviz, plot_importance

from yellowbrick.classifier import ClassificationReport, DiscriminationThreshold

from sklearn.experimental import enable_hist_gradient_boosting
#from sklearn.linear_model import ElasticNet, Lasso, LinearRegression, LogisticRegression, Ridge
#from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, ExtraTreesClassifier, ExtraTreesRegressor
from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, HistGradientBoostingClassifier, HistGradientBoostingRegressor


%matplotlib inline
sns.set_style('dark')
sns.set(font_scale=1.2)

from sklearn.metrics import silhouette_score, accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.feature_selection import RFE, RFECV, SelectKBest, f_classif, f_regression, chi2

from sklearn.inspection import permutation_importance
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler, OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.metrics import confusion_matrix, classification_report, mean_absolute_error, mean_squared_error,r2_score
from sklearn.metrics import plot_confusion_matrix, plot_precision_recall_curve, plot_roc_curve, accuracy_score
from sklearn.metrics import auc, f1_score, precision_score, recall_score, roc_auc_score


from imblearn.under_sampling import RandomUnderSampler
from imblearn.over_sampling import RandomOverSampler
from imblearn.over_sampling import SMOTE
from sklearn.cluster import KMeans
import warnings
warnings.filterwarnings('ignore')

import pickle
from pickle import dump, load

np.random.seed(0)

#from pycaret.classification import *
from pycaret.clustering import *


pd.set_option('display.max_columns',100)
#pd.set_option('display.max_rows',100)
pd.set_option('display.width', 1000)
np.set_printoptions(suppress=True)

%matplotlib inline

data = pd.read_csv('C:/Users/ganes/Downloads/archive (3)/marketing_campaign.csv',delimiter='\t')

data

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Z_CostContact	Z_Revenue	Response
0	5524	1957	Graduation	Single	58138.0	0	0	04-09-2012	58	635	88	546	172	88	88	3	8	10	4	7	0	0	0	0	0	0	3	11	1
1	2174	1954	Graduation	Single	46344.0	1	1	08-03-2014	38	11	1	6	2	1	6	2	1	1	2	5	0	0	0	0	0	0	3	11	0
2	4141	1965	Graduation	Together	71613.0	0	0	21-08-2013	26	426	49	127	111	21	42	1	8	2	10	4	0	0	0	0	0	0	3	11	0
3	6182	1984	Graduation	Together	26646.0	1	0	10-02-2014	26	11	4	20	10	3	5	2	2	0	4	6	0	0	0	0	0	0	3	11	0
4	5324	1981	PhD	Married	58293.0	1	0	19-01-2014	94	173	43	118	46	27	15	5	5	3	6	5	0	0	0	0	0	0	3	11	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2235	10870	1967	Graduation	Married	61223.0	0	1	13-06-2013	46	709	43	182	42	118	247	2	9	3	4	5	0	0	0	0	0	0	3	11	0
2236	4001	1946	PhD	Together	64014.0	2	1	10-06-2014	56	406	0	30	0	0	8	7	8	2	5	7	0	0	0	1	0	0	3	11	0
2237	7270	1981	Graduation	Divorced	56981.0	0	0	25-01-2014	91	908	48	217	32	12	24	1	2	3	13	6	0	1	0	0	0	0	3	11	0
2238	8235	1956	Master	Together	69245.0	0	1	24-01-2014	8	428	30	214	80	30	61	2	6	5	10	3	0	0	0	0	0	0	3	11	0
2239	9405	1954	PhD	Married	52869.0	1	1	15-10-2012	40	84	3	61	2	1	21	3	3	1	4	7	0	0	0	0	0	0	3	11	1

2240 rows × 29 columns

data.describe()

	ID	Year_Birth	Income	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Z_CostContact	Z_Revenue	Response
count	2240.000000	2240.000000	2216.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.0	2240.0	2240.000000
mean	5592.159821	1968.805804	52247.251354	0.444196	0.506250	49.109375	303.935714	26.302232	166.950000	37.525446	27.062946	44.021875	2.325000	4.084821	2.662054	5.790179	5.316518	0.072768	0.074554	0.072768	0.064286	0.013393	0.009375	3.0	11.0	0.149107
std	3246.662198	11.984069	25173.076661	0.538398	0.544538	28.962453	336.597393	39.773434	225.715373	54.628979	41.280498	52.167439	1.932238	2.778714	2.923101	3.250958	2.426645	0.259813	0.262728	0.259813	0.245316	0.114976	0.096391	0.0	0.0	0.356274
min	0.000000	1893.000000	1730.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
25%	2828.250000	1959.000000	35303.000000	0.000000	0.000000	24.000000	23.750000	1.000000	16.000000	3.000000	1.000000	9.000000	1.000000	2.000000	0.000000	3.000000	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
50%	5458.500000	1970.000000	51381.500000	0.000000	0.000000	49.000000	173.500000	8.000000	67.000000	12.000000	8.000000	24.000000	2.000000	4.000000	2.000000	5.000000	6.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
75%	8427.750000	1977.000000	68522.000000	1.000000	1.000000	74.000000	504.250000	33.000000	232.000000	50.000000	33.000000	56.000000	3.000000	6.000000	4.000000	8.000000	7.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
max	11191.000000	1996.000000	666666.000000	2.000000	2.000000	99.000000	1493.000000	199.000000	1725.000000	259.000000	263.000000	362.000000	15.000000	27.000000	28.000000	13.000000	20.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	3.0	11.0	1.000000

data.describe().T

	count	mean	std	min	25%	50%	75%	max
ID	2240.0	5592.159821	3246.662198	0.0	2828.25	5458.5	8427.75	11191.0
Year_Birth	2240.0	1968.805804	11.984069	1893.0	1959.00	1970.0	1977.00	1996.0
Income	2216.0	52247.251354	25173.076661	1730.0	35303.00	51381.5	68522.00	666666.0
Kidhome	2240.0	0.444196	0.538398	0.0	0.00	0.0	1.00	2.0
Teenhome	2240.0	0.506250	0.544538	0.0	0.00	0.0	1.00	2.0
Recency	2240.0	49.109375	28.962453	0.0	24.00	49.0	74.00	99.0
MntWines	2240.0	303.935714	336.597393	0.0	23.75	173.5	504.25	1493.0
MntFruits	2240.0	26.302232	39.773434	0.0	1.00	8.0	33.00	199.0
MntMeatProducts	2240.0	166.950000	225.715373	0.0	16.00	67.0	232.00	1725.0
MntFishProducts	2240.0	37.525446	54.628979	0.0	3.00	12.0	50.00	259.0
MntSweetProducts	2240.0	27.062946	41.280498	0.0	1.00	8.0	33.00	263.0
MntGoldProds	2240.0	44.021875	52.167439	0.0	9.00	24.0	56.00	362.0
NumDealsPurchases	2240.0	2.325000	1.932238	0.0	1.00	2.0	3.00	15.0
NumWebPurchases	2240.0	4.084821	2.778714	0.0	2.00	4.0	6.00	27.0
NumCatalogPurchases	2240.0	2.662054	2.923101	0.0	0.00	2.0	4.00	28.0
NumStorePurchases	2240.0	5.790179	3.250958	0.0	3.00	5.0	8.00	13.0
NumWebVisitsMonth	2240.0	5.316518	2.426645	0.0	3.00	6.0	7.00	20.0
AcceptedCmp3	2240.0	0.072768	0.259813	0.0	0.00	0.0	0.00	1.0
AcceptedCmp4	2240.0	0.074554	0.262728	0.0	0.00	0.0	0.00	1.0
AcceptedCmp5	2240.0	0.072768	0.259813	0.0	0.00	0.0	0.00	1.0
AcceptedCmp1	2240.0	0.064286	0.245316	0.0	0.00	0.0	0.00	1.0
AcceptedCmp2	2240.0	0.013393	0.114976	0.0	0.00	0.0	0.00	1.0
Complain	2240.0	0.009375	0.096391	0.0	0.00	0.0	0.00	1.0
Z_CostContact	2240.0	3.000000	0.000000	3.0	3.00	3.0	3.00	3.0
Z_Revenue	2240.0	11.000000	0.000000	11.0	11.00	11.0	11.00	11.0
Response	2240.0	0.149107	0.356274	0.0	0.00	0.0	0.00	1.0

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2240 entries, 0 to 2239
Data columns (total 29 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   ID                   2240 non-null   int64  
 1   Year_Birth           2240 non-null   int64  
 2   Education            2240 non-null   object 
 3   Marital_Status       2240 non-null   object 
 4   Income               2216 non-null   float64
 5   Kidhome              2240 non-null   int64  
 6   Teenhome             2240 non-null   int64  
 7   Dt_Customer          2240 non-null   object 
 8   Recency              2240 non-null   int64  
 9   MntWines             2240 non-null   int64  
 10  MntFruits            2240 non-null   int64  
 11  MntMeatProducts      2240 non-null   int64  
 12  MntFishProducts      2240 non-null   int64  
 13  MntSweetProducts     2240 non-null   int64  
 14  MntGoldProds         2240 non-null   int64  
 15  NumDealsPurchases    2240 non-null   int64  
 16  NumWebPurchases      2240 non-null   int64  
 17  NumCatalogPurchases  2240 non-null   int64  
 18  NumStorePurchases    2240 non-null   int64  
 19  NumWebVisitsMonth    2240 non-null   int64  
 20  AcceptedCmp3         2240 non-null   int64  
 21  AcceptedCmp4         2240 non-null   int64  
 22  AcceptedCmp5         2240 non-null   int64  
 23  AcceptedCmp1         2240 non-null   int64  
 24  AcceptedCmp2         2240 non-null   int64  
 25  Complain             2240 non-null   int64  
 26  Z_CostContact        2240 non-null   int64  
 27  Z_Revenue            2240 non-null   int64  
 28  Response             2240 non-null   int64  
dtypes: float64(1), int64(25), object(3)
memory usage: 507.6+ KB

#Spending variable creation
data['Age']=2014-data['Year_Birth']

data['Spending']=data['MntWines']+data['MntFruits']+data['MntMeatProducts']+data['MntFishProducts']+data['MntSweetProducts']+data['MntGoldProds']
data=data.rename(columns={'MntWines': "Wines",'MntFruits':'Fruits','MntMeatProducts':'Meat','MntFishProducts':'Fish','MntSweetProducts':'Sweets','MntGoldProds':'Gold'})

data

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	Wines	Fruits	Meat	Fish	Sweets	Gold	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Z_CostContact	Z_Revenue	Response	Age	Spending
0	5524	1957	Graduation	Single	58138.0	0	0	04-09-2012	58	635	88	546	172	88	88	3	8	10	4	7	0	0	0	0	0	0	3	11	1	57	1617
1	2174	1954	Graduation	Single	46344.0	1	1	08-03-2014	38	11	1	6	2	1	6	2	1	1	2	5	0	0	0	0	0	0	3	11	0	60	27
2	4141	1965	Graduation	Together	71613.0	0	0	21-08-2013	26	426	49	127	111	21	42	1	8	2	10	4	0	0	0	0	0	0	3	11	0	49	776
3	6182	1984	Graduation	Together	26646.0	1	0	10-02-2014	26	11	4	20	10	3	5	2	2	0	4	6	0	0	0	0	0	0	3	11	0	30	53
4	5324	1981	PhD	Married	58293.0	1	0	19-01-2014	94	173	43	118	46	27	15	5	5	3	6	5	0	0	0	0	0	0	3	11	0	33	422
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2235	10870	1967	Graduation	Married	61223.0	0	1	13-06-2013	46	709	43	182	42	118	247	2	9	3	4	5	0	0	0	0	0	0	3	11	0	47	1341
2236	4001	1946	PhD	Together	64014.0	2	1	10-06-2014	56	406	0	30	0	0	8	7	8	2	5	7	0	0	0	1	0	0	3	11	0	68	444
2237	7270	1981	Graduation	Divorced	56981.0	0	0	25-01-2014	91	908	48	217	32	12	24	1	2	3	13	6	0	1	0	0	0	0	3	11	0	33	1241
2238	8235	1956	Master	Together	69245.0	0	1	24-01-2014	8	428	30	214	80	30	61	2	6	5	10	3	0	0	0	0	0	0	3	11	0	58	843
2239	9405	1954	PhD	Married	52869.0	1	1	15-10-2012	40	84	3	61	2	1	21	3	3	1	4	7	0	0	0	0	0	0	3	11	1	60	172

2240 rows × 31 columns

data1=data[['Age','Education','Marital_Status','Income','Spending','Wines','Fruits','Meat','Fish','Sweets','Gold']]
data1.head()

	Age	Education	Marital_Status	Income	Spending	Wines	Fruits	Meat	Fish	Sweets	Gold
0	57	Graduation	Single	58138.0	1617	635	88	546	172	88	88
1	60	Graduation	Single	46344.0	27	11	1	6	2	1	6
2	49	Graduation	Together	71613.0	776	426	49	127	111	21	42
3	30	Graduation	Together	26646.0	53	11	4	20	10	3	5
4	33	PhD	Married	58293.0	422	173	43	118	46	27	15

X=data1.iloc[:,3:]
X.head()

	Income	Spending	Wines	Fruits	Meat	Fish	Sweets	Gold
0	58138.0	1617	635	88	546	172	88	88
1	46344.0	27	11	1	6	2	1	6
2	71613.0	776	426	49	127	111	21	42
3	26646.0	53	11	4	20	10	3	5
4	58293.0	422	173	43	118	46	27	15

X['Income'].describe()

count      2216.000000
mean      52247.251354
std       25173.076661
min        1730.000000
25%       35303.000000
50%       51381.500000
75%       68522.000000
max      666666.000000
Name: Income, dtype: float64

X['Income']=X['Income'].fillna(52247.2)

X.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2240 entries, 0 to 2239
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Income    2240 non-null   float64
 1   Spending  2240 non-null   int64  
 2   Wines     2240 non-null   int64  
 3   Fruits    2240 non-null   int64  
 4   Meat      2240 non-null   int64  
 5   Fish      2240 non-null   int64  
 6   Sweets    2240 non-null   int64  
 7   Gold      2240 non-null   int64  
dtypes: float64(1), int64(7)
memory usage: 140.1 KB

data1=X.sample(frac=1).reset_index(drop=True)

scores=[]
for k in range(2,10):
    model=KMeans(n_clusters=k,random_state=0).fit(data1)
    scores.append(silhouette_score(data1,model.labels_))

import plotly_express as px
px.line(y=scores,x=range(2,10),title='Silhouette Score for each K',labels={
    'x':'Values of K',
    'y':'silhouette_score'
})

from sklearn.cluster import KMeans
wcss = []
for i in range(1, 15):
    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)
    kmeans.fit(X)
    wcss.append(kmeans.inertia_)
plt.figure(figsize=(18,10), dpi=80)
plt.plot(range(1, 15), wcss)
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()

from sklearn.decomposition import PCA
pca = PCA(n_components = 2)
X = pca.fit_transform(X)

kmeans = KMeans(n_clusters = 3, init = 'k-means++', random_state = 42)
y_kmeans = kmeans.fit_predict(X)

plt.figure(figsize=(18,16), dpi=80)
plt.scatter(X[y_kmeans ==0, 0], X[y_kmeans == 0, 1], s = 20, c = 'red', label='cluster1')
plt.scatter(X[y_kmeans ==1, 0], X[y_kmeans == 1, 1], s = 20, c = 'blue', label='cluster2')
plt.scatter(X[y_kmeans ==2, 0], X[y_kmeans == 2, 1], s = 20, c = 'green', label='cluster3')
#plt.scatter(X[y_kmeans ==3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label='cluster4')
#plt.scatter(X[y_kmeans ==4, 0], X[y_kmeans == 4, 1], s = 100, c = 'gray', label='cluster5')
plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s = 300, c = 'yellow', label = 'centroids')
plt.title('The clusters of Customers')
plt.xlabel('Component1')
plt.ylabel('Component2')
plt.legend()

plt.show()

Data Visualization

K=3
model=KMeans(n_clusters=K,random_state=0).fit(data1)
data1['Cluster']=model.labels_
centers=model.cluster_centers_

fig=px.pie(values=data1['Cluster'].value_counts(),names=['Cluster 1', 'Cluster 2','Cluster3'],title='Number of people per Cluster')
fig.update_traces(
    textinfo='percent+value+label'
)
fig.show()

df = pd.DataFrame({'Cluster':data1['Cluster'],
                   'Spending':data1['Wines']+data1['Fruits']+data1['Meat']+data1['Fish']+data1['Sweets']+data1['Gold']})

px.scatter(df,y='Spending',color='Cluster',trendline='lowess',title='Total Spending',marginal_y='box')

import plotly.graph_objects as go
from plotly.subplots import make_subplots

tdf=pd.DataFrame({
    'Fruits':data1['Fruits'],
    'Gold':data1['Gold'],
    'Meat':data1['Meat'],
    'Wines':data1['Wines'],
    'Fish':data1['Fish'],
    'Sweets':data1['Sweets'],
    'Spending':data1['Fruits']+data1['Gold']+data1['Fish']+data1['Meat']+data1['Wines']+data1['Sweets'],
    'Cluster':data1['Cluster']
})


specs = [[{'type':'domain'}, {'type':'domain'}], 
         [{'type':'domain'}, {'type':'domain'}],
         [{'type':'domain'}, {'type':'domain'}]
        ]

fig = make_subplots(rows=3, cols=2, specs=specs,subplot_titles=['Fruits', 'Gold','Meat','Wines','Fish','Sweets'])


fig.add_trace(go.Pie(values=tdf['Fruits'],labels=tdf['Cluster']),1,1)
fig.add_trace(go.Pie(values=tdf['Gold'],labels=tdf['Cluster']),1,2)
fig.add_trace(go.Pie(values=tdf['Meat'],labels=tdf['Cluster']),2,1)
fig.add_trace(go.Pie(values=tdf['Wines'],labels=tdf['Cluster']),2,2)
fig.add_trace(go.Pie(values=tdf['Fish'],labels=tdf['Cluster']),3,1)
fig.add_trace(go.Pie(values=tdf['Sweets'],labels=tdf['Cluster']),3,2)

fig = go.Figure(fig)
fig.show()

data1.hist(bins=50, figsize=(20,15))
plt.suptitle('Feature' , x=0.5, y=1.00, ha='center', fontsize='large')
plt.tight_layout()
plt.show()

fig=px.scatter_3d(data1,x='Meat',y='Gold',z='Wines',color='Spending',color_continuous_scale='teal')
fig.update_layout(
    title={
        'text': 'Meat vs. Gold vs. Wines for Different Type of product Spending',
        'y':0.92,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'})
fig.show()

T : 1 Ans

---

1. In Cluster1, less number of people spend on Meat, while most spend on Gold.
2. In Cluster2, less number of people spend on Gold, while most spend on Meat.
3. In Cluster3, there is a outlaier.

Group 1 is the less spending one & Group 2 is the more spending one It is surpising to see that both trends are completely opposite. Cluster2 also does 86% of the spending.

#Correlation
plt.figure(figsize=(20,12))
sns.heatmap(data1.corr(),cmap="coolwarm",annot=True,fmt='.4f',linewidths=2)
plt.show()

Train Test Split

X = data.iloc[:,0:]
y = data.iloc[:,5]

X

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	Wines	Fruits	Meat	Fish	Sweets	Gold	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Z_CostContact	Z_Revenue	Response	Age	Spending
0	5524	1957	Graduation	Single	58138.0	0	0	04-09-2012	58	635	88	546	172	88	88	3	8	10	4	7	0	0	0	0	0	0	3	11	1	57	1617
1	2174	1954	Graduation	Single	46344.0	1	1	08-03-2014	38	11	1	6	2	1	6	2	1	1	2	5	0	0	0	0	0	0	3	11	0	60	27
2	4141	1965	Graduation	Together	71613.0	0	0	21-08-2013	26	426	49	127	111	21	42	1	8	2	10	4	0	0	0	0	0	0	3	11	0	49	776
3	6182	1984	Graduation	Together	26646.0	1	0	10-02-2014	26	11	4	20	10	3	5	2	2	0	4	6	0	0	0	0	0	0	3	11	0	30	53
4	5324	1981	PhD	Married	58293.0	1	0	19-01-2014	94	173	43	118	46	27	15	5	5	3	6	5	0	0	0	0	0	0	3	11	0	33	422
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2235	10870	1967	Graduation	Married	61223.0	0	1	13-06-2013	46	709	43	182	42	118	247	2	9	3	4	5	0	0	0	0	0	0	3	11	0	47	1341
2236	4001	1946	PhD	Together	64014.0	2	1	10-06-2014	56	406	0	30	0	0	8	7	8	2	5	7	0	0	0	1	0	0	3	11	0	68	444
2237	7270	1981	Graduation	Divorced	56981.0	0	0	25-01-2014	91	908	48	217	32	12	24	1	2	3	13	6	0	1	0	0	0	0	3	11	0	33	1241
2238	8235	1956	Master	Together	69245.0	0	1	24-01-2014	8	428	30	214	80	30	61	2	6	5	10	3	0	0	0	0	0	0	3	11	0	58	843
2239	9405	1954	PhD	Married	52869.0	1	1	15-10-2012	40	84	3	61	2	1	21	3	3	1	4	7	0	0	0	0	0	0	3	11	1	60	172

2240 rows × 31 columns

y

0       0
1       1
2       0
3       1
4       1
       ..
2235    0
2236    2
2237    0
2238    0
2239    1
Name: Kidhome, Length: 2240, dtype: int64

data

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	Wines	Fruits	Meat	Fish	Sweets	Gold	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Z_CostContact	Z_Revenue	Response	Age	Spending
0	5524	1957	Graduation	Single	58138.0	0	0	04-09-2012	58	635	88	546	172	88	88	3	8	10	4	7	0	0	0	0	0	0	3	11	1	57	1617
1	2174	1954	Graduation	Single	46344.0	1	1	08-03-2014	38	11	1	6	2	1	6	2	1	1	2	5	0	0	0	0	0	0	3	11	0	60	27
2	4141	1965	Graduation	Together	71613.0	0	0	21-08-2013	26	426	49	127	111	21	42	1	8	2	10	4	0	0	0	0	0	0	3	11	0	49	776
3	6182	1984	Graduation	Together	26646.0	1	0	10-02-2014	26	11	4	20	10	3	5	2	2	0	4	6	0	0	0	0	0	0	3	11	0	30	53
4	5324	1981	PhD	Married	58293.0	1	0	19-01-2014	94	173	43	118	46	27	15	5	5	3	6	5	0	0	0	0	0	0	3	11	0	33	422
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2235	10870	1967	Graduation	Married	61223.0	0	1	13-06-2013	46	709	43	182	42	118	247	2	9	3	4	5	0	0	0	0	0	0	3	11	0	47	1341
2236	4001	1946	PhD	Together	64014.0	2	1	10-06-2014	56	406	0	30	0	0	8	7	8	2	5	7	0	0	0	1	0	0	3	11	0	68	444
2237	7270	1981	Graduation	Divorced	56981.0	0	0	25-01-2014	91	908	48	217	32	12	24	1	2	3	13	6	0	1	0	0	0	0	3	11	0	33	1241
2238	8235	1956	Master	Together	69245.0	0	1	24-01-2014	8	428	30	214	80	30	61	2	6	5	10	3	0	0	0	0	0	0	3	11	0	58	843
2239	9405	1954	PhD	Married	52869.0	1	1	15-10-2012	40	84	3	61	2	1	21	3	3	1	4	7	0	0	0	0	0	0	3	11	1	60	172

2240 rows × 31 columns

Train Test Split

X = data.iloc[:,20:25]
y = data.iloc[:,28]

X

	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2
0	0	0	0	0	0
1	0	0	0	0	0
2	0	0	0	0	0
3	0	0	0	0	0
4	0	0	0	0	0
...	...	...	...	...	...
2235	0	0	0	0	0
2236	0	0	0	1	0
2237	0	1	0	0	0
2238	0	0	0	0	0
2239	0	0	0	0	0

2240 rows × 5 columns

y

0       1
1       0
2       0
3       0
4       0
       ..
2235    0
2236    0
2237    0
2238    0
2239    1
Name: Response, Length: 2240, dtype: int64

data.shape

(2240, 31)

Imbalance Data

y.value_counts()

0    1906
1     334
Name: Response, dtype: int64

na = RandomUnderSampler(sampling_strategy='majority',random_state=0)

new_X, new_y = na.fit_resample(X, y)

new_y.value_counts()

1    334
0    334
Name: Response, dtype: int64

new_X

	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2
0	0	0	0	0	0
1	0	0	0	0	0
2	0	0	0	0	0
3	0	0	0	0	0
4	0	0	0	0	0
...	...	...	...	...	...
663	1	0	0	0	0
664	1	0	0	0	0
665	0	0	0	0	0
666	0	1	1	0	0
667	0	0	0	0	0

668 rows × 5 columns

balanced classes

new_X.values, new_y.values

(array([[0, 0, 0, 0, 0],
        [0, 0, 0, 0, 0],
        [0, 0, 0, 0, 0],
        ...,
        [0, 0, 0, 0, 0],
        [0, 1, 1, 0, 0],
        [0, 0, 0, 0, 0]], dtype=int64),
 array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1], dtype=int64))

#train test split
X_train, X_test, y_train, y_test = train_test_split(new_X.values, new_y.values, test_size=0.2, random_state=0)

X_train.shape, X_test.shape, y_train.shape, y_test.shape

((534, 5), (134, 5), (534,), (134,))

Feature Scaling

X_train

array([[0, 0, 0, 0, 0],
       [1, 0, 1, 1, 0],
       [0, 0, 0, 0, 0],
       ...,
       [0, 0, 0, 0, 0],
       [1, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]], dtype=int64)

scaler = StandardScaler()

X_train_scaled = scaler.fit_transform(X_train)

X_test_scaled = scaler.transform(X_test)

X_train_scaled

array([[-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936,  2.45485555,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       ...,
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604]])

X_test_scaled

array([[ 2.61781229, -0.36572936,  2.45485555,  2.64010402,  5.20627152],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936,  2.45485555,  2.64010402, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594,  2.64010402, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594,  2.64010402, -0.19207604],
       [-0.38199836,  2.73426233,  2.45485555,  2.64010402, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936,  2.45485555,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936,  2.45485555,  2.64010402, -0.19207604],
       [-0.38199836,  2.73426233, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936,  2.45485555,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233,  2.45485555,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233,  2.45485555,  2.64010402, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936,  2.45485555,  2.64010402, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936,  2.45485555, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233, -0.40735594,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936,  2.45485555,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233,  2.45485555,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233,  2.45485555,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233,  2.45485555,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936,  2.45485555, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233,  2.45485555, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233,  2.45485555,  2.64010402, -0.19207604],
       [ 2.61781229, -0.36572936,  2.45485555, -0.37877295,  5.20627152],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [ 2.61781229, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233,  2.45485555, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936,  2.45485555, -0.37877295, -0.19207604],
       [-0.38199836,  2.73426233,  2.45485555,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936,  2.45485555,  2.64010402, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936,  2.45485555, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936,  2.45485555, -0.37877295, -0.19207604],
       [-0.38199836, -0.36572936, -0.40735594, -0.37877295, -0.19207604]])

Model Training

#from pycaret.regression import *
from pycaret.clustering import *
#from sklearn.impute import SimpleImputer 
from pycaret.classification import *

data = data.drop(['Dt_Customer'],1)

exp = setup(data = data, target = 'Response', session_id=0, normalize=True, train_size=0.8,imputation_type='iterative') 

	Description	Value
0	session_id	0
1	Target	Response
2	Target Type	Binary
3	Label Encoded	None
4	Original Data	(2240, 30)
5	Missing Values	True
6	Numeric Features	12
7	Categorical Features	17
8	Ordinal Features	False
9	High Cardinality Features	False
10	High Cardinality Method	None
11	Transformed Train Set	(1792, 110)
12	Transformed Test Set	(448, 110)
13	Shuffle Train-Test	True
14	Stratify Train-Test	False
15	Fold Generator	StratifiedKFold
16	Fold Number	10
17	CPU Jobs	-1
18	Use GPU	False
19	Log Experiment	False
20	Experiment Name	clf-default-name
21	USI	b605
22	Imputation Type	iterative
23	Iterative Imputation Iteration	5
24	Numeric Imputer	mean
25	Iterative Imputation Numeric Model	Light Gradient Boosting Machine
26	Categorical Imputer	constant
27	Iterative Imputation Categorical Model	Light Gradient Boosting Machine
28	Unknown Categoricals Handling	least_frequent
29	Normalize	True
30	Normalize Method	zscore
31	Transformation	False
32	Transformation Method	None
33	PCA	False
34	PCA Method	None
35	PCA Components	None
36	Ignore Low Variance	False
37	Combine Rare Levels	False
38	Rare Level Threshold	None
39	Numeric Binning	False
40	Remove Outliers	False
41	Outliers Threshold	None
42	Remove Multicollinearity	False
43	Multicollinearity Threshold	None
44	Remove Perfect Collinearity	True
45	Clustering	False
46	Clustering Iteration	None
47	Polynomial Features	False
48	Polynomial Degree	None
49	Trignometry Features	False
50	Polynomial Threshold	None
51	Group Features	False
52	Feature Selection	False
53	Feature Selection Method	classic
54	Features Selection Threshold	None
55	Feature Interaction	False
56	Feature Ratio	False
57	Interaction Threshold	None
58	Fix Imbalance	False
59	Fix Imbalance Method	SMOTE

 best_model = compare_models()

	Model	Accuracy	AUC	Recall	Prec.	F1	Kappa	MCC	TT (Sec)
lr	Logistic Regression	0.8878	0.8754	0.4280	0.6662	0.5146	0.4555	0.4729	0.7240
lda	Linear Discriminant Analysis	0.8862	0.8642	0.4826	0.6310	0.5420	0.4790	0.4871	0.0320
ridge	Ridge Classifier	0.8828	0.0000	0.3103	0.7039	0.4244	0.3707	0.4121	0.0170
catboost	CatBoost Classifier	0.8817	0.8689	0.3057	0.6791	0.4168	0.3626	0.4004	1.9250
et	Extra Trees Classifier	0.8806	0.8432	0.2866	0.6793	0.3997	0.3467	0.3876	0.1370
gbc	Gradient Boosting Classifier	0.8795	0.8665	0.3257	0.6463	0.4250	0.3676	0.3978	0.2370
lightgbm	Light Gradient Boosting Machine	0.8783	0.8592	0.3492	0.6194	0.4428	0.3816	0.4028	0.3510
rf	Random Forest Classifier	0.8778	0.8642	0.2237	0.7352	0.3351	0.2896	0.3549	0.1400
ada	Ada Boost Classifier	0.8744	0.8286	0.4002	0.5833	0.4665	0.3996	0.4127	0.0870
svm	SVM - Linear Kernel	0.8727	0.0000	0.4872	0.5789	0.5172	0.4458	0.4542	0.0190
xgboost	Extreme Gradient Boosting	0.8727	0.8511	0.3768	0.5798	0.4537	0.3859	0.3988	0.6260
knn	K Neighbors Classifier	0.8627	0.7229	0.1655	0.5497	0.2495	0.1993	0.2435	0.0490
dt	Decision Tree Classifier	0.8242	0.6503	0.4078	0.3886	0.3956	0.2934	0.2948	0.0190
qda	Quadratic Discriminant Analysis	0.3170	0.4682	0.6797	0.1306	0.2186	-0.0241	-0.0480	0.0230
nb	Naive Bayes	0.1892	0.5149	0.9682	0.1456	0.2530	0.0088	0.0395	0.0110

models()

	Name	Reference	Turbo
ID
lr	Logistic Regression	sklearn.linear_model._logistic.LogisticRegression	True
knn	K Neighbors Classifier	sklearn.neighbors._classification.KNeighborsCl...	True
nb	Naive Bayes	sklearn.naive_bayes.GaussianNB	True
dt	Decision Tree Classifier	sklearn.tree._classes.DecisionTreeClassifier	True
svm	SVM - Linear Kernel	sklearn.linear_model._stochastic_gradient.SGDC...	True
rbfsvm	SVM - Radial Kernel	sklearn.svm._classes.SVC	False
gpc	Gaussian Process Classifier	sklearn.gaussian_process._gpc.GaussianProcessC...	False
mlp	MLP Classifier	sklearn.neural_network._multilayer_perceptron....	False
ridge	Ridge Classifier	sklearn.linear_model._ridge.RidgeClassifier	True
rf	Random Forest Classifier	sklearn.ensemble._forest.RandomForestClassifier	True
qda	Quadratic Discriminant Analysis	sklearn.discriminant_analysis.QuadraticDiscrim...	True
ada	Ada Boost Classifier	sklearn.ensemble._weight_boosting.AdaBoostClas...	True
gbc	Gradient Boosting Classifier	sklearn.ensemble._gb.GradientBoostingClassifier	True
lda	Linear Discriminant Analysis	sklearn.discriminant_analysis.LinearDiscrimina...	True
et	Extra Trees Classifier	sklearn.ensemble._forest.ExtraTreesClassifier	True
xgboost	Extreme Gradient Boosting	xgboost.sklearn.XGBClassifier	True
lightgbm	Light Gradient Boosting Machine	lightgbm.sklearn.LGBMClassifier	True
catboost	CatBoost Classifier	catboost.core.CatBoostClassifier	True

compare = compare_models(exclude=['catboost','lightgbm','lda','qda','mlp','nb','ridge'],fold=5)

	Model	Accuracy	AUC	Recall	Prec.	F1	Kappa	MCC	TT (Sec)
lr	Logistic Regression	0.8895	0.8737	0.4291	0.6793	0.5250	0.4660	0.4823	0.0300
xgboost	Extreme Gradient Boosting	0.8789	0.8540	0.3501	0.6284	0.4481	0.3867	0.4082	0.4040
gbc	Gradient Boosting Classifier	0.8783	0.8581	0.3306	0.6478	0.4345	0.3743	0.4026	0.2060
et	Extra Trees Classifier	0.8756	0.8430	0.2362	0.6968	0.3464	0.2961	0.3520	0.1340
svm	SVM - Linear Kernel	0.8711	0.0000	0.3734	0.5698	0.4481	0.3794	0.3916	0.0200
ada	Ada Boost Classifier	0.8705	0.8335	0.3620	0.5625	0.4381	0.3697	0.3820	0.0840
rf	Random Forest Classifier	0.8700	0.8557	0.1928	0.6569	0.2953	0.2460	0.3043	0.1460
knn	K Neighbors Classifier	0.8577	0.7140	0.1376	0.4900	0.2125	0.1617	0.2020	0.0680
dt	Decision Tree Classifier	0.8370	0.6848	0.4723	0.4316	0.4506	0.3553	0.3560	0.0200

xgboost = create_model('xgboost', fold = 5)

	Accuracy	AUC	Recall	Prec.	F1	Kappa	MCC
0	0.8942	0.8864	0.4706	0.6857	0.5581	0.5004	0.5118
1	0.8691	0.8491	0.3137	0.5714	0.4051	0.3384	0.3577
2	0.8659	0.8260	0.2800	0.5385	0.3684	0.3017	0.3220
3	0.8687	0.8236	0.2941	0.5769	0.3896	0.3246	0.3479
4	0.8966	0.8846	0.3922	0.7692	0.5195	0.4683	0.5018
Mean	0.8789	0.8540	0.3501	0.6284	0.4481	0.3867	0.4082
SD	0.0135	0.0273	0.0716	0.0861	0.0759	0.0812	0.0814

print(xgboost)

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,
              importance_type='gain', interaction_constraints='',
              learning_rate=0.300000012, max_delta_step=0, max_depth=6,
              min_child_weight=1, missing=nan, monotone_constraints='()',
              n_estimators=100, n_jobs=-1, num_parallel_tree=1,
              objective='binary:logistic', random_state=0, reg_alpha=0,
              reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='auto',
              use_label_encoder=True, validate_parameters=1, verbosity=0)

tuned_xgboost = tune_model(xgboost, optimize='F1', fold=5)

	Accuracy	AUC	Recall	Prec.	F1	Kappa	MCC
0	0.8663	0.8887	0.6078	0.5254	0.5636	0.4852	0.4870
1	0.8301	0.8673	0.4902	0.4167	0.4505	0.3507	0.3524
2	0.8436	0.8271	0.5800	0.4531	0.5088	0.4174	0.4219
3	0.8408	0.8265	0.4902	0.4464	0.4673	0.3739	0.3745
4	0.8883	0.8843	0.6471	0.6000	0.6226	0.5572	0.5577
Mean	0.8538	0.8587	0.5631	0.4883	0.5226	0.4369	0.4387
SD	0.0209	0.0271	0.0632	0.0663	0.0635	0.0756	0.0753

print(tuned_xgboost)

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=0.5, gamma=0, gpu_id=-1,
              importance_type='gain', interaction_constraints='',
              learning_rate=0.15, max_delta_step=0, max_depth=6,
              min_child_weight=1, missing=nan, monotone_constraints='()',
              n_estimators=200, n_jobs=-1, num_parallel_tree=1,
              objective='binary:logistic', random_state=0, reg_alpha=0.05,
              reg_lambda=5, scale_pos_weight=31.900000000000006, subsample=0.7,
              tree_method='auto', use_label_encoder=True, validate_parameters=1,
              verbosity=0)

# AUC-ROC plot
# plot_model(tuned_rf) (or) plot_model(tuned_rf, plot = 'auc') --> both gives same result
plot_model(tuned_xgboost, plot = 'auc')

plot_model(tuned_xgboost)

plot_model(tuned_xgboost, plot='threshold')

# Validation Curve
plot_model(tuned_xgboost, plot='vc')

plot_model(tuned_xgboost, plot = 'confusion_matrix')

plot_model(tuned_xgboost, plot='feature')

Evaluate our Model

# evaluate model
evaluate_model(tuned_xgboost)

Interpret Model

xgboost = create_model('xgboost')

	Accuracy	AUC	Recall	Prec.	F1	Kappa	MCC
0	0.8722	0.8487	0.3462	0.6000	0.4390	0.3727	0.3907
1	0.9056	0.9056	0.5385	0.7368	0.6222	0.5697	0.5789
2	0.8547	0.8735	0.4000	0.4762	0.4348	0.3522	0.3539
3	0.8547	0.8348	0.2400	0.4615	0.3158	0.2435	0.2599
4	0.8380	0.8239	0.3600	0.4091	0.3830	0.2902	0.2910
5	0.8883	0.8221	0.4000	0.6667	0.5000	0.4415	0.4598
6	0.8715	0.8171	0.3200	0.5714	0.4103	0.3445	0.3628
7	0.8436	0.8177	0.2400	0.4000	0.3000	0.2181	0.2271
8	0.8771	0.8979	0.3462	0.6429	0.4500	0.3877	0.4114
9	0.9218	0.8693	0.5769	0.8333	0.6818	0.6389	0.6530
Mean	0.8727	0.8511	0.3768	0.5798	0.4537	0.3859	0.3988
SD	0.0254	0.0318	0.1049	0.1368	0.1152	0.1271	0.1283

# interpret_model: SHAP
interpret_model(xgboost)

# interpret model : Correlation
interpret_model(xgboost, plot='correlation')

interpret_model(xgboost, plot='reason', observation=0)

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interpret_model(xgboost, plot='reason', observation=2)

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interpret_model(xgboost, plot='reason')

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Using XGBoost (Scikit-Learn) Using RandomSearchCV

model = XGBClassifier(random_state=0, n_estimators=100, objective='binary:logistic')

parameters = {'max_depth': np.arange(3,10,1),
              'eta': np.arange(0.05,0.3,0.05),
              'n_estimators':np.arange(100,1000,100),
              'min_child_weight': np.arange(1,4,1),
              'gamma':np.arange(0,10,2),
              'subsample':np.arange(0.5,0.9,0.1),
              'colsample_bytree':np.arange(0.5,0.9,0.1),
              'reg_alpha':np.arange(0,1,0.1),
              'reg_lambda':np.arange(0,1,0.1)
             }

randm = RandomizedSearchCV(estimator=model, param_distributions = parameters, cv = 5, n_iter = 10, 
                           n_jobs=-1, scoring='f1')

randm.fit(new_X,new_y)

RandomizedSearchCV(cv=5, error_score=nan,
                   estimator=XGBClassifier(base_score=None, booster=None,
                                           colsample_bylevel=None,
                                           colsample_bynode=None,
                                           colsample_bytree=None, gamma=None,
                                           gpu_id=None, importance_type='gain',
                                           interaction_constraints=None,
                                           learning_rate=None,
                                           max_delta_step=None, max_depth=None,
                                           min_child_weight=None, missing=nan,
                                           monotone_constraints=None,
                                           n...
                                        'min_child_weight': array([1, 2, 3]),
                                        'n_estimators': array([100, 200, 300, 400, 500, 600, 700, 800, 900]),
                                        'reg_alpha': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]),
                                        'reg_lambda': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]),
                                        'subsample': array([0.5, 0.6, 0.7, 0.8])},
                   pre_dispatch='2*n_jobs', random_state=None, refit=True,
                   return_train_score=False, scoring='f1', verbose=0)

randm.best_estimator_

XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=0.5, eta=0.2, gamma=8,
              gpu_id=-1, importance_type='gain', interaction_constraints='',
              learning_rate=0.200000003, max_delta_step=0, max_depth=7,
              min_child_weight=2, missing=nan, monotone_constraints='()',
              n_estimators=400, n_jobs=8, num_parallel_tree=1,
              objective='binary:logistic', random_state=0, reg_alpha=0.0,
              reg_lambda=0.1, scale_pos_weight=1, subsample=0.6,
              tree_method='exact', use_label_encoder=True,
              validate_parameters=1, verbosity=None)

score

score = randm.best_score_

final_score = score * 100
print('Score : \n', final_score,'%')

Score : 
 64.55002512341962 %

T : 2 Ans

---

• On the promotion done by the company. Run SHAP analysis on the model results, and my recommendation to business for the next round of campaigns is Gold .
• Acording to shap distribution we should focuse on Gold campaigns.